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Field
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to integrate large and complex molecular datasets with information at individual level, with specific attention to open and reproducible research, e.g., in the development of codes and algorithms. We will focus
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: 272342957 Lead Research Software Engineer US-NJ-Princeton Job ID: 2024-19506 Type: Full-Time # of Openings: 1 Category: Information Technology Overview The Princeton Language and Intelligence Initiative
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of navigation systems Algorithmic development Independently lead specific tasks within project scope Required skills/knowledge include: Strong written, oral, and interpersonal communication skills Experience
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novel algorithms for pattern detection, extreme event attribution, and seasonal forecasting. Lead development of innovative visualization techniques and interpretable machine learning methods (30%). Drive
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science, including supervising team members, developing novel algorithms for pattern detection, extreme event attribution, and seasonal forecasting. Lead development of innovative visualization techniques
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formula is true or false (EXPTIME vs NP). Can we develop and implement efficient algorithms for this problem? This problem has been attacked using multiple different methods for the past 40 years, without
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Cornell faculty members and the project’s co-principal investigators (from multiple organizations) to oversee a cross-organizational research team consisting of a group of postdoctoral associates, PhD
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challenges, encompassing both fundamental and applied research, from the development of algorithms, tools, and frameworks that advance scientific discovery to methodologies that utilise computational
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Immunobiology research. Develop software, creating query routines, and building relational databases. Possess advanced knowledge of algorithms used to solve problems based on biological information and data using
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Simon Fraser University | Northern British Columbia Fort Nelson, British Columbia | Canada | about 1 month ago
at all levels—developing new materials, designing creative and interactive technologies, engineering future hardware platforms like quantum computers, and writing the algorithms that power machine learning